引用本文: 李勇发, 左小清, 朱大明, 吴文豪, 布金伟, 李永宁, 顾晓娜, 张荐铭, 黄成. 基于张量分解的时序InSAR影像降维压缩方法[J]. 武汉大学学报 ( 信息科学版).
LI Yong-fa, ZUO Xiao-qing, ZHU Da-ming, WU Wen-hao, BU Jin-wei, LI Yong-ning, GU Xiao-na, ZHANG Jian-ming, HUANG Cheng. Dimensionality Reduction and Compression Method for Time Series InSAR Images Based on Tensor Decomposition[J]. Geomatics and Information Science of Wuhan University.
 Citation: LI Yong-fa, ZUO Xiao-qing, ZHU Da-ming, WU Wen-hao, BU Jin-wei, LI Yong-ning, GU Xiao-na, ZHANG Jian-ming, HUANG Cheng. Dimensionality Reduction and Compression Method for Time Series InSAR Images Based on Tensor Decomposition[J]. Geomatics and Information Science of Wuhan University.

## Dimensionality Reduction and Compression Method for Time Series InSAR Images Based on Tensor Decomposition

• 摘要: 随着SAR影像的增加，时序InSAR数据处理量呈指数增长，给广域长时序地表形变监测带来了新的挑战，特别是分布式目标InSAR（DS-InSAR）技术中所有干涉图均参与计算，对计算和存储资源要求较高，一定程度限制了其发展和应用推广。然而，时序InSAR影像在时间维和空间维均存在冗余信息，降维压缩是去除冗余信息的有效方法之一。本文提出一种基于张量分解的时序InSAR影像降维压缩方法，根据较小子空间内各像素之间具有相似的统计特性，将较小子空间范围内的协方差矩阵表示成三阶张量形式，采用Tucker分解算法同时实现时间维和空间维的降维压缩处理。选取昆明市主城区Sentinel-1A数据进行实验验证分析，结果表明，当压缩子空间为2×3和2×5时，计算效率分别提升约24倍和40倍，且能够满足监测精度需求。当压缩子空间为2×10和2×15时，信息丢失增多，但仍然能识别出形变位置且其计算效率分别提升约80倍和120倍。研究成果为广域长时序InSAR地表形变监测提供了新的数据处理方法。

Abstract: Objectives: With the increase of SAR images, the processing capacity of time-series InSAR data is exponential growth, which brings new challenges to the monitoring of wide Area long term surface deformation monitoring. Especially in distributed scatters InSAR (DS-InSAR) technology, all interferograms are involved in computation, which requires high computing and storage resources, which to some extent limits its development and application promotion. However, time-series InSAR images have redundant information in both temporal and spatial dimensions, dimensionality reduction compression is one of the effective methods for removing redundant information. Methods: Due to the inability of matrices to meet data processing requirements, a tensor with unique advantages in storing and processing high-dimensional data is introduced, and a temporal InSAR image dimensionality reduction compression method based on tensor decomposition is proposed. According to the similar statistical properties between the pixels in the small subspace, the covariance matrix in the small subspace is expressed as a third-order tensor form. The Tucker decomposition algorithm is used to realize the time dimension and space dimension reduction and compression processing at the same time. Results: Taking Sentinel-1A image data as an example for experimental verification and analysis, the results show that: (1) The deformation spatial position obtained after image compression is highly consistent with that before image compression and the PS InSAR method, and the deformation rate value is similar, indicating that the proposed compression method is feasible and has high reliability. (2) When the compressed subspace is 2×3 and 2×5, the computational efficiency is improved by about 24 times and 40 times respectively, and it can meet the monitoring accuracy requirements. When the compressed subspace is 2×10 and 2×15, the information loss increased, but the deformation position could still be identified and its computational efficiency increased by about 80 times and 120 times, respectively. Therefore, in practical applications, the selection of compressed subspace size should be based on computational efficiency and monitoring refinement. Conclusions: The research results provide a new data processing method for wide-area and long-term surface deformation monitoring, which can effectively improve the computational efficiency of time-series InSAR.

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